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Algorithms and Software

Algorithms

Sliding Organ RegistrationTraditional registration methods assume a smooth deformation field and enforce that smoothness via isotropic regularization.   We have devised a registration method that allows for sliding in the direction in the tangential direction at surfaces.
  • D. F. Pace, M. Niethammer, and S. R. Aylward, “Sliding geometries in deformable image registration,” in MICCAI’11: Proceedings of the Third international conference on Abdominal Imaging, Berlin, Heidelberg, 2011, pp. 141–148. 
  • Source code available in TubeTK
Geometric MetamorphosisTraditional registration methods assume a single deformation field to account for differences between images.  However, if a pathology changes via infiltration or recession, then the pathology's deformation field may be locally discontinuous compared to the background deformation field which accounts for tissue displacement induced by the pathology.   Geometric Metamorphosis simultaneously solves for both deformation fields.
  • M. Niethammer, G. L. Hart, D. F. Pace, P. M. Vespa, A. Irimia, J. D. Van Horn, and S. R. Aylward, “Geometric metamorphosis,” in MICCAI’11: Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention, Berlin, Heidelberg, 2011, pp. 639–646.
  • Source code available in CalaTK
Vessel SegmentationThe extraction of the centerlines of tubular objects intwo and three-dimensional images is a part of many clinical image analysis tasks. One common approach to tubular object centerline extraction is based on intensity ridge traversal. We have developed a multi-scale traversal technique that is insensitive to noise, intensity and spatial variations common to vascular networks and a variety of imaging modalities.
  • S. R. Aylward and E. Bullitt, “Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction,” Medical Imaging, IEEE Transactions on, vol. 21, no. 2, pp. 61–75, 2002. 
  • Source code available in TubeTK
Vessel-based RegistrationOur method aligns a source image with a target image by registering a model of the tubes in the source image directly with the target image. Time can be spent to extract an accurate model of the tubes in the source image. Multiple target images can then be registered with that model without additional extractions. Our registration method builds upon the principles of our tubular object segmentation work that combines dynamic-scale central ridge traversal with radius estimation. In particular, our registration method’s consistency stems from incorporating multi-scale ridge and radius measures into the model-image match metric. Additionally, the method’s speed is due in part to the use of coarse-to-fine optimization strategies that are enabled by measures made during model extraction and by the parameters inherent to the model-image match metric.
  • S. Aylward, J. Jomier, S. Weeks, and E. Bullitt, “Registration and analysis of vascular images,” INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 55, no. 2–3, pp. 123–138, Dec. 2003. 
  • Source code available in TubeTK
Spatial Graphs: Vascular Network CharacterizationOur interest in characterizing intra-cranial vasculature arises from the mounting evidence that a genetic relationship exists between mental disorders and vascular network formation. It has been established that during development vascular endothelial growth factors not only spur vessel and tissue growth but also direct tissue differentiation and the formation of organs.

Graph methods that summarize vasculature by its branching topology are not sufficient for the statistical characterization of a population of intra-cranial vascular networks. Intra-cranial vascular networks are typified by topological variations and long, wandering paths between branch points. We have developed a graph-based representation, called spatial graphs, that captures both the branching patterns and the spatial locations of vascular networks. Furthermore, we have developed companion methods that allow spatial graphs to (1) statistically characterize populations of vascular networks, (2) generate the central vascular net- work of a population of vascular networks, and (3) distinguish between populations of vascular networks.
  • S. Aylward, J. Jomier, C. Vivert, V. LeDigarcher, and E. Bullitt, “Spatial graphs for intra-cranial vascular network characterization, generation, and discrimination,” in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2005, PT 1, 2005, vol. 3749, pp. 59–66. 
  • Source code to be released with TubeTK (in the future)
Vessel Tortuosity Characterization
 
The clinical recognition of abnormal vasculartortuosity, or excessive bending, twisting, and winding, isimportant to the diagnosis of many diseases. Automated detectionand quantitation of abnormal vascular tortuosity from three-dimensional (3D) medical image data would therefore be of value.
  • E. Bullitt, D. Zeng, B. Mortamet, A. Ghosh, S. R. Aylward, W. Lin, B. L. Marks, and K. Smith, “The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography,” NEUROBIOLOGY OF AGING, vol. 31, no. 2, pp. 290–300, Feb. 2010. 
  • Source code to be released with TubeTK (in the future)


Highlighted Software

 


TubeTK is an open-source toolkit for the segmentation, registration, and analysis of tubes and surfaces in images.

Tubes and surfaces, as generalized 1D and 2D manifolds in N-dimensional images, are essential components in a variety of image analysis tasks. Instances of tubular structures in images include blood vessels in magnetic resonance angiograms and b-mode ultrasound images, wires in microscopy images of integrated circuits, roads in aerial photographs, and nerves in confocal microscopy.

A guiding premise of TubeTK is that by focusing on 1D and 2D manifolds we can devise methods that are insensitive to the modality, noise, contrast, and scale of the images being analyzed and to the arrangement and deformations of the objects in them.  In particular, we propose that TubeTK's manifold methods offer improved performance for many applications, compared to methods involving the analysis of independent geometric measures (e.g., edges and corners) or requiring complete shape models.

TubeTK is implemented as a C++ library and makes extensive use of ITK and VTK.  Select methods of TubeTK are provided as command-line applications and python functions.

 



ParaView Glance is a light-weight, open-source, web application developed at Kitware for visualizing volumetric images, molecular structures, geometric objects, and point clouds.

As part of the ParaView platform, it is intended to allow users to quickly glance at their small to medium size data. Ultimately, ParaView Glance is intended to help you:

  1. Quickly view your data on your computer. It launches quickly and supports a wide variety of data formats so that you can quickly look at the most recent results from a new algorithm you are developing or visually confirm the contents of a data file on your drive.

  2. Review items-of-interest on Girder. Imagine that you have reviewed a large collection of data on Girder, and you have narrowed your search to two interesting items that you want to explore more closely. Via an optional Girder module, ParaViewGlance can be associated with appropriate types of data items and launched to download and view items of interest with minimal interaction (a single click per item), minimal delay (requiring only the time to transfer the data to your machine), and no need to explicitly install an application or run a dedicated server (ParaViewGlance is a stand-alone, JavaScript+WebAssembly application).

  3. Develop new applications involving VTK.js and ITK.js. ParaView Glance has been architected to be a highly customizable platform. It can be used as a basis for creating stand-alone web-based applications, desktop applications via Electron, and web-based systems that involve services from ParaViewWeb and/or Resonant.